Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions.

中文翻译：

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使用 RZWQM2 模拟华北平原麦田的土壤含水量和作物生长指标

土壤含水量（SWC）是限制农田生态系统中作物生长和产量的重要因素。土壤水分的观测和模拟对提高水分利用效率和作物产量有很大帮助。本研究在华北平原山东禹城农业生态系统国家观测研究站进行。研究期间横跨冬小麦（Triticum aestivumL.) 2017 年至 2019 年的生长阶段。使用宇宙射线中子探测器监测连续的每日 SWC。此外，还确定了冬小麦的作物叶面积指数 (LAI)、产量和地上生物量。根区质量模型 2 (RZWQM2) 用于模拟和验证 SWC、作物 LAI、产量和地上生物量。结果表明，2017-2019年小麦生育期和成熟期SWC的模拟误差很小。2017-2018年冬小麦拔节期SWC模拟的均方根误差（RMSE）和相对均方根误差（RRMSE）分别为0.0296和0.1605，2018-2019年分别为0.0265和0.1480。在受雨影响的日子里，2017-2018 年的 RMSE（0.0253）和 RRMSE（0.0980）显着低于 2018-2019 年（0.0301 和 0.1458，分别），表明与雨季相比，雨季在旱季降低了模型精度。模拟的 LAI 显着高于测量值。2017-2018年和2018-2019年冬小麦模拟产量值分别比实测产量低5.61%和3.92%。2017-2018年地上生物量模拟值明显低于实测值（45.48%）。本研究表明，与 2018-2019 年干冷小麦生长期相比，2017-2018 年较高的降水和温度导致 SWC 和作物生长成分的模拟较差。本研究表明，年异常降雨和温度对 SWC 和小麦生长的模拟有显着影响，尤其是在气候变化强烈的胁迫条件下。表明与雨季相比，雨季在旱季降低了模型的准确性。模拟的 LAI 显着高于测量值。2017-2018年和2018-2019年冬小麦模拟产量值分别比实测产量低5.61%和3.92%。2017-2018年地上生物量模拟值明显低于实测值（45.48%）。本研究表明，与 2018-2019 年干冷小麦生长期相比，2017-2018 年较高的降水和温度导致 SWC 和作物生长成分的模拟较差。本研究表明，年异常降雨和温度对 SWC 和小麦生长的模拟有显着影响，尤其是在气候变化强烈的胁迫条件下。表明与雨季相比，雨季在旱季降低了模型的准确性。模拟的 LAI 显着高于测量值。2017-2018年和2018-2019年冬小麦模拟产量值分别比实测产量低5.61%和3.92%。2017-2018年地上生物量模拟值明显低于实测值（45.48%）。本研究表明，与 2018-2019 年干冷小麦生长期相比，2017-2018 年较高的降水和温度导致 SWC 和作物生长成分的模拟较差。本研究表明，年度异常降雨和温度对 SWC 和小麦生长的模拟有显着影响，尤其是在气候变化强烈的胁迫条件下。